Variations in lighting impact the quality of images captured by cameras. For example, normal objects in sunlight and in a shadow often differ in brightness by a factor of 10,000 or more. Objects deep in a room, seen through a small window from outside, can be very dark compared to the outside wall of the house illuminated by direct sunlight. Cameras use adjustable dynamic ranges to account for these differences, where a photographer chooses the range of radiance values of interest and selects the exposure time to optimally capture colors in that range.
To capture the dynamic range in a scene with variations in lighting, a photographer can use high dynamic range (“HDR”) imaging. HDR imaging allows a greater dynamic range of luminance between light and dark areas of a scene than conventional imaging techniques. HDR images are generated by capturing multiple images at different exposures (e.g., using different F-stops, ISO values, and/or shutter speeds) with a conventional camera, and then combining the image data from the multiple images into a single HDR image.
However, the different exposures used to capture a dynamic range image could fail to account for variations in lighting in all areas of a scene, particularly if different light sources or shadows in an area result in a wide range of lighting conditions. A user could capture multiple images and then manually analyze the images at a later time to assess the exposure values used for HDR imaging and thereby select a set of appropriate exposure values. But this cumbersome solution reduces the ability of a photographer to quickly identify and capture an interesting scene in real time (i.e., when the photographer actually uses the camera to capture an image). For these and other reasons, existing HDR processes present disadvantages.